AMD is planning to launch its Instinct MI400 series in the second half of 2026, marking the “Red Team’s” first rack-scale architecture product. The system will include two rack-level solutions: the MI450X IF128 and the MI450X IF64. These products are directly targeting NVIDIA’s Vera Rubin VR200 NVL144 and the GB200/GB300 Blackwell series.
Key Features and Interconnect Technology #
The core highlight of the Instinct MI400 series lies in its interconnect technology and high-bandwidth network design. The MI450X IF128 cluster utilizes the Infinity Fabric over Ethernet protocol as its expansion domain, capable of connecting 128 GPUs, with each GPU offering over 1.8TB/s of unidirectional bandwidth. This design enhances communication efficiency between GPUs, making it particularly suitable for large-scale AI training and inference tasks. To further optimize horizontal scaling communication, AMD equips each GPU with three Pensando 800GbE network cards, providing a total network bandwidth of 2.4Tbit/s, which is 50% higher than NVIDIA’s VR200 NVL144’s 1.6Tbit/s. Additionally, AMD offers an alternative configuration that allows for connection via PCIe interfaces using two custom Ethernet cards. However, due to PCIe 5.0 bandwidth limitations, this configuration cannot fully unleash the network card’s performance but provides customers with a more flexible option.
Product Offerings and Architecture #
In contrast, the MI450X IF64 cluster focuses on a single-rack design, adopting a simpler backplane architecture that reduces manufacturing complexity and deployment risks while retaining high-performance characteristics. This product is better suited for small-to-medium scale AI inference systems or high-performance computing tasks and will be a key focus for AMD in market promotion. Both solutions are based on AMD’s next-generation CDNA architecture, optimized for AI and HPC workloads. The MI450X is designed for low-precision AI computations (such as FP4, FP8, BF16), while the concurrently launched MI430X targets high-precision HPC computations (such as FP32, FP64), maximizing chip performance through functional differentiation.
Comparison with NVIDIA and Cost Optimization #
NVIDIA’s GB200 NVL72 and GB300 NVL72 currently dominate the market with their NVLink technology. A single rack can connect 72 GPUs, providing up to 1.4 exaFLOPS of AI computing power and 30TB of fast memory, making them particularly suitable for real-time inference of trillion-parameter large models. NVIDIA also achieves efficient cross-rack expansion through its Quantum-X800 InfiniBand and Spectrum-X800 Ethernet platforms, with network speeds up to 800Gb/s. Meanwhile, AMD’s newly revealed MI450 series aims to reduce system costs and power consumption through higher network bandwidth and a passive copper cable topology design.
Challenges and Strategic Initiatives #
However, market concerns exist regarding the complexity of the MI450X IF128 design, suggesting potential difficulties in mass production. This is AMD’s first foray into the rack-level market, requiring it to contend with NVIDIA’s mature advantages in software ecosystem (e.g., CUDA) and supply chain integration. To address this, AMD continues to optimize its ROCm open software stack, supporting mainstream AI frameworks like PyTorch and Hugging Face, and adding features such as FP8 format and Flash Attention 3 to improve model compatibility and performance. Furthermore, AMD has strengthened its networking technology and system integration capabilities through the acquisitions of ZT Systems and Pensando, supporting the deployment of the MI400 series.
Industry Trends and AMD’s Positioning #
In terms of industry trends, the AI hardware market is growing rapidly. According to market analysis, the global AI accelerator market is projected to exceed $500 billion by 2027, with NVIDIA currently holding approximately 77% of the AI chip market share. AMD, through the differentiated positioning of its MI400 series, particularly its competitiveness in cost-sensitive enterprises and the open-source ecosystem, is expected to attract a portion of the customer base. For example, the MI325X has already demonstrated performance comparable to NVIDIA’s H200 in Llama2-70B tests, and the MI400 series’ higher memory capacity (expected to reach 256GB HBM3E) and bandwidth (6TB/s) will further enhance its support for large language models.
Strategic Significance #
The launch of the AMD Instinct MI400 series is not only a technological breakthrough but also a reflection of its strategic positioning. By providing high-performance, low-cost rack-level solutions, AMD is striving to gain a larger share in the AI and HPC markets. Despite facing production and competitive pressures, the innovative design of the MI450X IF128 and IF64 opens up new market opportunities for AMD, and its future performance is worth watching.